Prospect Markets teams up with ASAPI.AI to test ML-driven prediction models for event markets
Prospect Prediction Markets Inc. announced a limited-scope collaboration with ASAPI.AI to design and evaluate machine-learning models for event-driven prediction markets. The work is focused on proof-of-concept: build, test, and pressure-check models that could later inform production use on the Prospect Markets platform if they prove valuable.
The initiative emphasizes model evaluation and market applicability over flashy demos. No securities are being issued as part of this collaboration, and there is no guarantee of commercialization.
Why this matters for engineers
Prediction markets are a clean testbed for probabilistic modeling. You get streaming signals, explicit probabilities, and real feedback loops-ideal for calibration, signal detection, and understanding how information moves prices.
ASAPI.AI brings experience in autonomous model training and cloud-scale experimentation. The team includes Dr. Qingchen Wang (Kaggle Grandmaster) and founding advisor Dr. Stefano Ermon, Professor of Computer Science at Stanford University, with a track record in probabilistic modeling and decision-making under uncertainty.
What the technical scope likely includes
- Data and features: historical outcomes, market order books, liquidity and spread dynamics, injury/news feeds, in-game telemetry, and derived features (momentum, volatility, price impact).
- Modeling approaches: calibrated classifiers and regressors, gradient boosting, sequence models, temporal point processes, and ensembles; where relevant, Bayesian methods for uncertainty and stability under shift.
- Online learning and drift handling: incremental updates, decay strategies, covariate shift detection, and dynamic recalibration (e.g., isotonic or temperature scaling).
- Evaluation: strict train/test separation by time, leak checks, backtesting with walk-forward validation, and metrics tied to decisions-Brier score, log loss, expected calibration error, and PnL-style simulations.
- Market impact and safety: adversarial testing against manipulation, thin-liquidity scenarios, tail events, and guardrails to prevent feedback loops that amplify model bias.
- Infrastructure: cloud-native training pipelines, streaming ingestion, a feature store, reproducible runs, lineage tracking, and cost-aware scaling.
- Explainability and governance: traceable feature attributions, audit logs, model cards, and policy hooks for compliance reviews.
- Integration path: sandboxed paper trading, A/B tests on live-like traffic, kill-switches, and progressive rollout controls.
Signals of success
Measured improvements in calibration, faster convergence after new information, and better signal-to-noise on market moves. Evidence through backtests that stands up in paper trading and limited live trials, with clear operational playbooks for rollback and monitoring.
Prospect's CTO highlighted a disciplined, narrow-scope approach that keeps the platform's long-term scalability front and center. That framing matters: start small, validate quickly, scale what works.
Who's involved
ASAPI.AI was founded by Dr. Qingchen Wang, whose work spans probabilistic inference, large-scale modeling, and applied ML systems. Dr. Stefano Ermon, a Professor of Computer Science at Stanford University, is a founding advisor with research across probabilistic modeling, deep learning, and AI for decision-making under uncertainty.
Practical takeaways for your team
- Ship a baseline first: logistic regression or gradient boosting with strict time-based splits and clean leakage checks. Beat that before you reach for heavier models.
- Treat calibration as a feature: track Brier, log loss, and ECE. Apply post-hoc calibration and test per-segment (sport, league, liquidity band).
- Build a backtesting harness: walk-forward evaluation, event-timestamp integrity, and realistic fill/latency assumptions.
- Stress-test data integrity: simulate sparse markets, stale feeds, and adversarial price moves. Add detectors for anomalies and drift.
- Put observability first: feature drift alerts, prediction drift dashboards, cost telemetry, and per-model service-level targets.
- Gate releases: paper trade, then guarded rollouts with A/B comparisons and hard stop conditions.
If you want a structured way to level up the skills behind probabilistic modeling, experimentation, and evaluation, explore the AI Learning Path for Data Scientists.
About Prospect Markets
Prospect Markets operates a sports-focused prediction market and fan engagement platform. It offers transparent, real-time markets across sports to turn passive viewership into active participation and produce data-rich signals on fan expectations.
Forward-looking information
This initiative is exploratory. There is no assurance that proof-of-concept work will lead to a commercial agreement or deployment. Risks include challenges in AI development and integration, model viability, cybersecurity, and regulatory or policy changes affecting prediction markets and AI. For additional risk factors, see the Company's continuous disclosure filings on SEDAR+.
Learn more
Company site: prospectmarkets.com
Your membership also unlocks:
